Brian's Blog

In last month’s State of the Union Address, the President highlighted the importance of education in enhancing America’s competitiveness through the 21stCentury. In particular he highlighted theP-TECH program, in which IBM has been championing and investing in New York and Chicago.

In addition to such transformational programs, there are many challenges facing school systems all across America each and every day. These range from new students, new policies and procedures, and annual budgetary concerns. Traditional education systems, designed to prepare children for the 19th century industrial economy, did their job well. In the 21st century however, the economy is quickly changing and requires different skills and ways of learning. Globalization requires education systems to compete for resources in a much larger and diverse marketplace. Changing demographics place high demands on education systems to dynamically respond to shifts in student and teacher needs. Employers and governments are raising expectations for student matriculation and achievement, especially in science, technology and math. Affordable and pervasive consumer technology is changing the way students gain knowledge.

The challenges facing local, state, and federal departments of education today are daunting—changes in technology, commerce, politics and demographics demand new approaches to teaching and learning. If we hope to help our students achieve their potential—and realize the potential of a smarter planet—then schools will have to become more instrumented, interconnected, and intelligent.

Academic and operational performance outcomes are a result of a complex interplay of many variables. Programs, staff, demographics, curriculum, testing style, funding, class size, and school size are just some of the possible factors affecting student achievement. When data is scattered across an organization, in paper files or spreadsheets, solving the puzzle of success and failure is next to impossible. That’s why many school boards and districts are turning to big data and analytics to correlate and analyze their own data.

●Accelerate research discovery and innovation capabilities using Analytics for managing research

To meet these imperatives, big data and advanced analytics provides education stakeholders with the tools to address these imperatives. The growth and velocity of data is outpacing the ability for education organizations to cost-effectively manage it. There is a variety of data involved in education, including student performance, teacher performance, institutional performance, test scores, attendance rates, financial details, supply chain, and operational data. Unstructured data includes notes, research data, web data, RTLS feeds, and more. The key for organizations is to be able to bring all these varieties of data into one analytic environment.

While in most cases, the data is already valuable on its own, the real transformative effect of the data will come when it is combined with other types to get a 360° view of students, schools, and school systems. Many organizations are looking to analyze the whole body of student education data, structured and unstructured, to provide better evidence-based, student-centric attention, with better outcomes and lower costs.

Until recently, the traditional approach to analytics involved business users determining what questions to ask and IT stakeholders structuring the data to answer the questions. Now, a new paradigm reigns in the Big Data world where IT delivers the platform to enable creative discovery and leaders and educators can explore what questions can be asked. Whereas the traditional approach uses structured and repeatable analytics such as queries on data at rest, the Big Data approach involves iterative, exploratory, and autonomic analytics on data in motion where insight drives answers.

Because of these shifts and challenges in the analytics landscape, education stakeholders need to think and work differently. Based on our research and the Federal Big Data Commission Report, we have identified the following key best practices and policies for implementation of big data and analytics across an education organization:

●Start with a clear mission or business requirement, and fully define a discrete set of use cases.

●Take inventory and understand your data assets. Enact common IT standards for student tracking and reporting. Create a unique persistent view of students and schools over time.

●Assess your current set of capabilities and technical architecture against what is required to support your initial use cases and develop a consistent and pervasive framework for student and system assessments.

●Explore which data assets can be exposed for public consumption to drive research and innovation.

●Incorporate data-driven decision making into the institutional culture.

●Leverage analytics to identify opportunities for interventions,

A successful implementation of these policies hinges on the use of big data technology. A sample of these transformations include:

●Single View of the Student data systems

●Aggregated data from multiple systems into reporting and classroom dashboards

●Predictive analytics for early interventions

●Teacher, student, and system performance reporting tools

In future blogs, I will dive a little deeper into each of these areas to explore how advanced analytics and big data can further America’s competitiveness via education.